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      • >02_bar_plot
    • Python 3 (ipykernel)
      • >session_day2
    • Python 3 (ipykernel)
      • >indexing and data structure
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      Terminals

        06_array

        1. 1D array
        2. 2D array
        3. in 2D array when we talk about the axis first we include the column and then row e.g(2,4) in this 2 is the column and 4 is the row
        4. 3D array
        /Desktop/data_viz/
        Name
        ...
        Last Modified
        File Size
        • 01_line_plotyesterday310.4 KB
        • 02_bar_plotyesterday266.8 KB
        • 03_boxplot22 hours ago239 KB
        • 04_otherplots8 hours ago228.1 KB
        • 05_interactive5 hours ago3.7 MB
        • 06_array43 seconds ago14.8 KB
        Kernel status: Idle
        # learn python by dr ammar
        ## we learn how to use jupyter notebook
        ### basics of python


        **01- my first program**


        learn python by dr ammar¶

        we learn how to use jupyter notebook¶

        basics of python¶

        01- my first program

        [ ]:

        [10]:
        # my first program
        print(2+3)
        print("hello world")
        print("asad and fareeha")

        5
hello world
asad and fareeha
        [ ]:
        **02-operators**
        [12]:
        #operators function
        print(2+3)
        print(3-1)
        print(3*2)
        print(4/2) #single division given the value in float
        print(13%2)
        print(6//2) #double divide goven the value in int type
        #using the power function by using double sterric
        print(2**3)
        #now we form the equation
        print(3**2/2*3/3+6-4)

        5
2
6
2.0
1
3
8
6.5
        *now we see sequence with which the code is running
        PEDMAS
        paranthesis exponenet division multiplication divsion addition subtraction
        this process is running according left_to_right*

        now we see sequence with which the code is running PEDMAS paranthesis exponenet division multiplication divsion addition subtraction this process is running according left_to_right

        **03-string**

        03-string

        [15]:
        print("hello world")
        print("python with asad")
        print("asad is learning python from ammar")
        #we can write the string in the sigle ,double or tripple quotes
        print('hi')
        print("helloo")
        print('''how are you''')
        #spaces are effect inside the string there is no effect of space inside the string
        print("what's up")
        hello world
python with asad
asad is learning python from ammar
hi
helloo
how are you
what's up
        Kernel status: Idle
        # -indexing
        ## to find the location of the number or digits

        -indexing¶

        to find the location of the number or digits¶

        [2]:
        # make a string
        a="asad ibadat"
        a
        [2]:
        'asad ibadat'
        [4]:
        a
        [4]:
        'asad ibadat'
        [8]:
        a[0]
        [8]:
        'a'
        [12]:
        a[10]
        [12]:
        't'
        [14]:
        a[5]
        [14]:
        'i'
        [16]:
        a[2]
        [16]:
        'a'
        [18]:
        a[7]
        [18]:
        'a'
        [20]:
        a[9]
        [20]:
        'a'
        [22]:
        a[8]
        [22]:
        'd'
        [26]:
        b="fareeha"
        b
        [26]:
        'fareeha'
        [28]:
        b[4]
        [28]:
        'e'
        [30]:
        b[6]
        [30]:
        'a'
        [37]:
        c="i am learning python by dr ammar"
        c
        [37]:
        'i am learning python by dr ammar'
        [41]:
        c[15]
        [41]:
        'y'
        [43]:
        c[6]
        [43]:
        'e'
        [45]:
        c[7]
        [45]:
        'a'
        [59]:
        # last index is exclusive
        a[0:10]
        [59]:
        'asad ibada'
        [51]:
        a[0:11]
        [51]:
        'asad ibadat'
        [53]:
        b[0:7]
        [53]:
        'fareeha'
        [55]:
        c[0:15]
        [55]:
        'i am learning p'
        [57]:
        c[0:17]
        [57]:
        'i am learning pyt'
        [61]:
        a[-5:1]
        [61]:
        ''
        [65]:
        a[-2]
        [65]:
        'a'
        [69]:
        a[-1]
        [69]:
        't'
        [71]:
        a[-10]
        [71]:
        's'
        [73]:
        a[-6:-1]
        [73]:
        'ibada'
        # -length of index
        # mean how many number of digits in the index

        -length of index¶

        mean how many number of digits in the index¶

        [33]:
        len(b)
        [33]:
        7
        [35]:
        len(a)
        [35]:
        11
        [47]:
        len(c)
        [47]:
        32
        # string methods

        string methods¶

        [77]:
        food="biryani"
        food
        [77]:
        'biryani'
        [79]:
        len(food)
        [79]:
        7
        [119]:
        # upper case letter
        food.upper()
        [119]:
        'BIRYANI'
        [117]:
        # lower case letter
        food.lower()
        [117]:
        'biryani'
        [115]:
        # capatalize every letter
        food.capitalize()
        [115]:
        'Biryani'
        [113]:
        # replace the letter
        food.replace("b","sh")
        [113]:
        'shiryani'
        [105]:
        lover="asad and fareeha"
        lover
        [105]:
        'asad and fareeha'
        [107]:
        lover.capitalize()
        [107]:
        'Asad and fareeha'
        [109]:
        lover.lower()
        [109]:
        'asad and fareeha'
        [111]:
        lover.replace("asad","fareeha")
        [111]:
        'fareeha and fareeha'
        [125]:
        # counting the specific alpahbet in the string
        name="asad with fareeha and fareeha with asad"
        name
        [125]:
        'asad with fareeha and fareeha with asad'
        [123]:
        name.count("a")
        [123]:
        9
        [127]:
        name.count("f")
        [127]:
        2
        [129]:
        name.count("i")
        [129]:
        2
        # -finding the index number in the string

        -finding the index number in the string¶

        [132]:
        attraction="asad has the attraction for the fareeha"
        attraction
        [132]:
        'asad has the attraction for the fareeha'
        [134]:
        attraction.find("a")
        [134]:
        0
        [136]:
        attraction.find("tt")
        [136]:
        14
        # -split the string

        -split the string¶

        [144]:
        piyar="asad fareeha say mohbat karta ha"
        piyar
        [144]:
        'asad fareeha say mohbat karta ha'
        [146]:
        piyar.split("a")
        [146]:
        ['', 's', 'd f', 'reeh', ' s', 'y mohb', 't k', 'rt', ' h', '']
        [148]:
        piyar.split("e")
        [148]:
        ['asad far', '', 'ha say mohbat karta ha']
        # bsic data structure in python
        ## 01 tuple
        ## 02 list
        ## 03 dictionaries
        ## 04 set

        bsic data structure in python¶

        01 tuple¶

        02 list¶

        03 dictionaries¶

        04 set¶

        ## 1- tuple
        - ordered collection of elements
        - enclosed in the () round braces
        - different kinds of elemnt can be stored
        - onece element can store you can not change them

        1- tuple¶

        • ordered collection of elements
        • enclosed in the () round braces
        • different kinds of elemnt can be stored
        • onece element can store you can not change them
        [172]:
        tup1= (1, "python", True, 2.5)
        tup1
        [172]:
        (1, 'python', True, 2.5)
        [174]:
        tup=(12,45.5,"hello")
        tup
        [174]:
        (12, 45.5, 'hello')
        [176]:
        tup2=("fari is mine","i love her too much",143)
        tup2
        [176]:
        ('fari is mine', 'i love her too much', 143)
        [178]:
        #type of tuple
        type(tup)
        [178]:
        tuple
        [182]:
        type(tup2)
        [182]:
        tuple
        [184]:
        type(tup1)
        [184]:
        tuple
        ### -indexing in python

        -indexing in python¶

        [191]:
        tup[1]
        [191]:
        45.5
        [193]:
        tup[2]
        [193]:
        'hello'
        [195]:
        tup1[1]
        [195]:
        'python'
        [197]:
        tup2[1]
        [197]:
        'i love her too much'
        [200]:
        tup2[0]
        [200]:
        'fari is mine'
        [202]:
        tup1[0:5]
        [202]:
        (1, 'python', True, 2.5)
        [204]:
        tup[0:2]
        [204]:
        (12, 45.5)
        [208]:
        # last elemnt is exculsive
        tup2[0:3]
        [208]:
        ('fari is mine', 'i love her too much', 143)
        [210]:
        #counts of element in tuple
        len(tup2)
        [210]:
        3
        [212]:
        len(tup1)
        [212]:
        4
        [214]:
        len(tup)
        [214]:
        3
        [220]:
        # adding of the tuple
        tup + tup1 + tup2
        [220]:
        (12,
 45.5,
 'hello',
 1,
 'python',
 True,
 2.5,
 'fari is mine',
 'i love her too much',
 143)
        [242]:
        # in this we add and repeat the tuple
        tup*2 + tup2
        [242]:
        (12,
 45.5,
 'hello',
 12,
 45.5,
 'hello',
 'fari is mine',
 'i love her too much',
 143)
        [222]:
        # minimum value of the tuple
        tup3=(10,20,30,40,50,60,70)
        tup3
        [222]:
        (10, 20, 30, 40, 50, 60, 70)
        [224]:
        min(tup3)
        [224]:
        10
        [230]:
        tup4=(33,44,55,66,77,88,99,00)
        tup4
        [230]:
        (33, 44, 55, 66, 77, 88, 99, 0)
        [232]:
        min(tup4)
        [232]:
        0
        [234]:
        # to find the maximum value in the tuple
        tup5=(23,34,56,78,99)
        tup5
        [234]:
        (23, 34, 56, 78, 99)
        [236]:
        max(tup5)
        [236]:
        99
        [238]:
        tup6=(88,98,76,44,22)
        tup6
        [238]:
        (88, 98, 76, 44, 22)
        [240]:
        max(tup6)
        [240]:
        98
        ---
        # 02 list
        - ordered collection of elements
        - enclosed in the [] square brackets
        - you can change the value

        02 list¶

        • ordered collection of elements
        • enclosed in the [] square brackets
        • you can change the value
        [247]:
        list1=[1,"asad",False]
        list
        [247]:
        list
        [249]:
        list2=[2,"fareeha",True]
        list2
        [249]:
        [2, 'fareeha', True]
        [255]:
        #type of list
        type(list1)
        [255]:
        list
        [253]:
        type(list2)
        [253]:
        list
        [257]:
        #adding of list
        list1+list2
        [257]:
        [1, 'asad', False, 2, 'fareeha', True]
        [259]:
        #adding and reapeting of list
        list1*2 + list2
        [259]:
        [1, 'asad', False, 1, 'asad', False, 2, 'fareeha', True]
        [265]:
        #to find the length of the list
        len(list1)
        [265]:
        3
        [267]:
        len(list2)
        [267]:
        3
        [271]:
        #indexing is going here
        list1[1]

        [271]:
        'asad'
        [273]:
        list1[2]
        [273]:
        False
        [277]:
        list2[1]
        [277]:
        'fareeha'
        [299]:
        #reverse function
        list1.reverse()
        list1
        [299]:
        [1, 'asad', False]
        [285]:
        list2.reverse()
        list2
        [285]:
        [True, 'fareeha', 2]
        [287]:
        #clear function
        list2.clear()
        list2
        [287]:
        []
        [297]:
        #copy function
        list1.copy()
        list1
        [297]:
        [False, 'asad', 1]
        [291]:
        list3=[12,90,65,78,34,13,55]
        list3
        [291]:
        [12, 90, 65, 78, 34, 13, 55]
        [295]:
        #sort function
        list3.sort()
        list3
        [295]:
        [12, 13, 34, 55, 65, 78, 90]
        [305]:
        #this function is attact strings or variavles
        list1.append("i want to spent my whole life with fari")
        list1
        [305]:
        [1,
 'asad',
 False,
 'i want to spent my whole life with fari',
 'i want to spent my whole life with fari',
 'i want to spent my whole life with fari']
        [317]:
        #count function in the string
        list1.count("asad")
        [317]:
        1
        [319]:
        #to find the length of list
        len(list3)
        [319]:
        7
        [323]:
        #we can repeat the list
        list3*2 + list1
        [323]:
        [12,
 13,
 34,
 55,
 65,
 78,
 90,
 12,
 13,
 34,
 55,
 65,
 78,
 90,
 1,
 'asad',
 False,
 'i want to spent my whole life with fari',
 'i want to spent my whole life with fari',
 'i want to spent my whole life with fari']
        [325]:
        list=list1+list2
        list
        [325]:
        [1,
 'asad',
 False,
 'i want to spent my whole life with fari',
 'i want to spent my whole life with fari',
 'i want to spent my whole life with fari']
        [ ]:

        [333]:
        list4=["asad", "with"]
        list4
        [333]:
        ['asad', 'with']
        [337]:
        list5=["fari"]
        list5
        [337]:
        ['fari']
        [339]:
        list6= list4+list5
        list6
        [339]:
        ['asad', 'with', 'fari']
        ## -dictionaries
        - unordered collection of elements
        - keys and values
        - enclosed in the curly{} brackets
        - you can change the value

        -dictionaries¶

        • unordered collection of elements
        • keys and values
        • enclosed in the curly{} brackets
        • you can change the value
        [349]:
        # food and their prices
        food1={"samosa":30,"pakora":40,"raita":50,"salad":30,"chicken rolls":20}
        food1
        [349]:
        {'samosa': 30, 'pakora': 40, 'raita': 50, 'salad': 30, 'chicken rolls': 20}
        [369]:
        #type of food variable
        type(food1)
        [369]:
        dict
        [367]:
        #length of food variable
        len(food1)
        [367]:
        5
        [373]:
        #separate function of key
        keys=food1.keys()
        keys
        [373]:
        dict_keys(['samosa', 'pakora', 'raita', 'salad', 'chicken rolls'])
        [375]:
        #separate function of value
        values=food1.values()
        values
        [375]:
        dict_values([30, 40, 50, 30, 20])
        [383]:
        #adding the new element in the dictionaries
        food1["tikki"]=10
        food1
        [383]:
        {'samosa': 30,
 'pakora': 40,
 'raita': 50,
 'salad': 30,
 'chicken rolls': 20,
 'tikki': 10,
 'ice': 10,
 'dahibhali': 100}
        [379]:
        food1["ice"]=10
        food1
        [379]:
        {'samosa': 30,
 'pakora': 40,
 'raita': 50,
 'salad': 30,
 'chicken rolls': 20,
 'tikki': 10,
 'ice': 10}
        [381]:
        food1["dahibhali"]=100
        food1
        [381]:
        {'samosa': 30,
 'pakora': 40,
 'raita': 50,
 'salad': 30,
 'chicken rolls': 20,
 'tikki': 10,
 'ice': 10,
 'dahibhali': 100}
        [387]:
        #updating the value
        food1["ice"]=15
        food1
        [387]:
        {'samosa': 30,
 'pakora': 40,
 'raita': 50,
 'salad': 30,
 'chicken rolls': 20,
 'tikki': 10,
 'ice': 15,
 'dahibhali': 100}
        [391]:
        food2={"dates":200,"chocalates":300,"sawanya":500}
        food2
        [391]:
        {'dates': 200, 'chocalates': 300, 'sawanya': 500}
        [395]:
        #adding the both list of food
        food1.update(food2)
        food1
        [395]:
        {'samosa': 30,
 'pakora': 40,
 'raita': 50,
 'salad': 30,
 'chicken rolls': 20,
 'tikki': 10,
 'ice': 15,
 'dahibhali': 100,
 'dates': 200,
 'chocalates': 300,
 'sawanya': 500}
        # -set
        - unordered and unindexed
        - curly braces{} are used
        - no duplicate is allowed

        -set¶

        • unordered and unindexed
        • curly braces{} are used
        • no duplicate is allowed
        [398]:
        s1={1,5.5,"asad","fareeha",True}
        s1
        [398]:
        {1, 5.5, 'asad', 'fareeha'}
        [404]:
        #this is the add function and boolen words are not copied in the set
        s1.add("lover")
        s1
        [404]:
        {1, 5.5, 'asad', 'fareeha', 'lover'}
        [406]:
        s1.clear()
        s1
        [406]:
        set()
        [416]:
        s1.update("fareeha","ali")
        s1
        [416]:
        {'a', 'e', 'f', 'h', 'i', 'l', 'r'}
        Kernel status: Idle
        [3]:
        # import the libararies\
        import seaborn as sns
        import matplotlib.pyplot as plt

        # load the data set
        phool = sns.load_dataset("iris")
        phool
        [3]:
        sepal_length sepal_width petal_length petal_width species
        0 5.1 3.5 1.4 0.2 setosa
        1 4.9 3.0 1.4 0.2 setosa
        2 4.7 3.2 1.3 0.2 setosa
        3 4.6 3.1 1.5 0.2 setosa
        4 5.0 3.6 1.4 0.2 setosa
        ... ... ... ... ... ...
        145 6.7 3.0 5.2 2.3 virginica
        146 6.3 2.5 5.0 1.9 virginica
        147 6.5 3.0 5.2 2.0 virginica
        148 6.2 3.4 5.4 2.3 virginica
        149 5.9 3.0 5.1 1.8 virginica

        150 rows × 5 columns

        [33]:
        # import the libararies\
        import seaborn as sns
        import matplotlib.pyplot as plt

        # load the data set
        phool = sns.load_dataset("iris")
        phool

        # draw the bar plot with the color
        sns.barplot(x="species" , y="petal_length" ,hue="sepal_length", data=phool )
        plt.show()
        [21]:
        # now we replace the y with sepal_width
        # import the libararies\
        import seaborn as sns
        import matplotlib.pyplot as plt

        # load the data set
        phool = sns.load_dataset("iris")
        phool

        # draw the bar plot
        sns.barplot(x="species" , y="sepal_width" , data=phool )
        plt.show()
        [15]:
        # for the grouping and now we using the data set of the titanic
        [17]:
        # this is code is used to check what is in the data set like(titanic)
        # import the libararies\
        import seaborn as sns
        import matplotlib.pyplot as plt

        # load the data set
        kashti = sns.load_dataset("titanic")
        kashti


        [17]:
        survived pclass sex age sibsp parch fare embarked class who adult_male deck embark_town alive alone
        0 0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
        1 1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
        2 1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
        3 1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
        4 0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True
        ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
        886 0 2 male 27.0 0 0 13.0000 S Second man True NaN Southampton no True
        887 1 1 female 19.0 0 0 30.0000 S First woman False B Southampton yes True
        888 0 3 female NaN 1 2 23.4500 S Third woman False NaN Southampton no False
        889 1 1 male 26.0 0 0 30.0000 C First man True C Cherbourg yes True
        890 0 3 male 32.0 0 0 7.7500 Q Third man True NaN Queenstown no True

        891 rows × 15 columns

        [35]:

        # import the libararies\
        import seaborn as sns
        import matplotlib.pyplot as plt

        # load the data set
        kashti = sns.load_dataset("titanic")
        kashti


        # draw the bar plot
        sns.barplot(x="who" , y="alone" ,hue="deck", data=kashti )
        plt.show()
        [37]:
        #now we set the order
        [45]:
        # this is code is used to check what is in the data set like(titanic)
        # import the libararies\
        import seaborn as sns
        import matplotlib.pyplot as plt

        # load the data set
        kashti = sns.load_dataset("titanic")
        kashti

        # draw the bar plot and set the order
        sns.barplot(x="who" , y="alone" ,hue="deck", data=kashti , order=[ "child","man","woman"] )
        plt.show()

        [47]:
        # now we used the coloring
        [59]:

        # import the libararies\
        import seaborn as sns
        import matplotlib.pyplot as plt

        # load the data set
        kashti = sns.load_dataset("titanic")
        kashti


        # draw the bar plot
        sns.barplot(x="who" , y="alone" ,hue="sex", data=kashti ,order=["woman","child","man"], color="darkgrey" )
        plt.show()
        C:\Users\Rana Azam\AppData\Local\Temp\ipykernel_14116\127388509.py:11: FutureWarning: 

Setting a gradient palette using color= is deprecated and will be removed in v0.14.0. Set `palette='dark:darkgrey'` for the same effect.

  sns.barplot(x="who" , y="alone" ,hue="sex", data=kashti ,order=["woman","child","man"], color="darkgrey"  )
        [61]:
        # now we remove the bar in the plot
        [63]:
        # this is code is used to check what is in the data set like(titanic)
        # import the libararies\
        import seaborn as sns
        import matplotlib.pyplot as plt

        # load the data set
        kashti = sns.load_dataset("titanic")
        kashti

        # draw the bar plot and remove the black bar from the plot by using the confidence interval
        sns.barplot(x="who" , y="alone" ,hue="deck", data=kashti , order=[ "child","man","woman"], ci=None )
        plt.show()

        C:\Users\Rana Azam\AppData\Local\Temp\ipykernel_14116\2431501591.py:11: FutureWarning: 

The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.

  sns.barplot(x="who" , y="alone" ,hue="deck", data=kashti , order=[ "child","man","woman"], ci=None )
        [65]:
        # now we used the different types of palette
        [67]:
        # this is code is used to check what is in the data set like(titanic)
        # import the libararies\
        import seaborn as sns
        import matplotlib.pyplot as plt

        # load the data set
        kashti = sns.load_dataset("titanic")
        kashti

        # draw the bar plot and set the order
        sns.barplot(x="who" , y="alone" ,hue="deck", data=kashti , order=[ "child","man","woman"],ci=None,
        palette="pastel")
        plt.show()

        C:\Users\Rana Azam\AppData\Local\Temp\ipykernel_14116\2145264662.py:11: FutureWarning: 

The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.

  sns.barplot(x="who" , y="alone" ,hue="deck", data=kashti , order=[ "child","man","woman"],ci=None,
        [69]:
        # for the estimator we import the librarie of the numpy
        [77]:

        # import the libararies\
        import seaborn as sns
        from numpy import median
        import matplotlib.pyplot as plt

        # load the data set
        kashti = sns.load_dataset("titanic")
        kashti

        # draw the bar plot and set the order
        sns.barplot(x="who" , y="fare" ,hue="deck", data=kashti , order=[ "child","man","woman"] , estimator=median,ci=None,palette="pastel")
        plt.show()

        C:\Users\Rana Azam\AppData\Local\Temp\ipykernel_14116\2451678822.py:11: FutureWarning: 

The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.

  sns.barplot(x="who" , y="fare" ,hue="deck", data=kashti , order=[ "child","man","woman"] , estimator=median,ci=None,palette="pastel")
        [79]:
        # for the color intensity we also used the saturation
        [89]:

        # import the libararies\
        import seaborn as sns
        from numpy import median
        import matplotlib.pyplot as plt

        # load the data set
        kashti = sns.load_dataset("titanic")
        kashti

        # draw the bar plot and set the saturation intensity of the color
        sns.barplot(x="who" , y="fare" ,hue="deck", data=kashti , order=[ "child","man","woman"] , estimator=median,ci=None,
        saturation=1)
        plt.show()

        C:\Users\Rana Azam\AppData\Local\Temp\ipykernel_14116\2056478644.py:11: FutureWarning: 

The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.

  sns.barplot(x="who" , y="fare" ,hue="deck", data=kashti , order=[ "child","man","woman"] , estimator=median,ci=None,
        # set the style
        - this is for the line plot

        set the style¶

        • this is for the line plot
        [102]:
        import seaborn as sns
        import matplotlib.pyplot as plt
        phool=sns.load_dataset("iris")
        sns.lineplot(x="sepal_length",y="species",data=phool)
        sns.set_style("darkgrid")
        [106]:
        # now we form the horizantal plot
        [108]:

        # import the libararies\
        import seaborn as sns
        from numpy import median
        import matplotlib.pyplot as plt

        # load the data set
        kashti = sns.load_dataset("titanic")
        kashti

        # draw the bar plot and set numeric value on the x axis and categorical value on the y axis
        sns.barplot(x="fare" , y="who" ,hue="deck", data=kashti , order=[ "child","man","woman"] , estimator=median,ci=None,
        saturation=1)
        plt.show()

        C:\Users\Rana Azam\AppData\Local\Temp\ipykernel_14116\122115732.py:11: FutureWarning: 

The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.

  sns.barplot(x="fare" , y="who" ,hue="deck", data=kashti , order=[ "child","man","woman"] , estimator=median,ci=None,
        [140]:
        # import the libararies\
        import seaborn as sns
        import matplotlib.pyplot as plt

        # load the data set
        kashti = sns.load_dataset("titanic")
        kashti

        # draw the bar plot and set numeric value on the x axis and categorical value on the y axis
        sns.barplot(x="class" , y="fare" ,hue="deck", data=kashti ,
        linewidth=5,facecolor=(.6,.7,.8,.9),
        errcolor="0",edgecolor="0.3")
        sns.set_style("white")
        plt.show()

        C:\Users\Rana Azam\AppData\Local\Temp\ipykernel_14116\756062696.py:10: FutureWarning: 

The `errcolor` parameter is deprecated. And will be removed in v0.15.0. Pass `err_kws={'color': '0'}` instead.

  sns.barplot(x="class" , y="fare" ,hue="deck", data=kashti ,
        Kernel status: Idle
        [12]:
        # import the libraray
        import seaborn as sns

        # set the style
        sns.set_style("whitegrid")

        #load the data set
        kashti = sns.load_dataset("titanic")

        # darw the box plot
        sns.boxplot(x="class", y="fare" ,color="red",data=kashti)
        [12]:
        <Axes: xlabel='class', ylabel='fare'>
        [14]:
        # for another data set
        [18]:
        # this code is used for the to check what is in the data set
        # import the libraray
        import seaborn as sns

        # set the style
        sns.set_style("whitegrid")

        #load the data set
        tip = sns.load_dataset("tips")
        tip
        [18]:
        total_bill tip sex smoker day time size
        0 16.99 1.01 Female No Sun Dinner 2
        1 10.34 1.66 Male No Sun Dinner 3
        2 21.01 3.50 Male No Sun Dinner 3
        3 23.68 3.31 Male No Sun Dinner 2
        4 24.59 3.61 Female No Sun Dinner 4
        ... ... ... ... ... ... ... ...
        239 29.03 5.92 Male No Sat Dinner 3
        240 27.18 2.00 Female Yes Sat Dinner 2
        241 22.67 2.00 Male Yes Sat Dinner 2
        242 17.82 1.75 Male No Sat Dinner 2
        243 18.78 3.00 Female No Thur Dinner 2

        244 rows × 7 columns

        [20]:
        # import the libraray
        import seaborn as sns

        # set the style
        sns.set_style("whitegrid")

        #load the data set
        tip = sns.load_dataset("tips")
        tip
        # darw the box plot
        sns.boxplot(x="day", y="tip" ,color="red",data=tip)
        [20]:
        <Axes: xlabel='day', ylabel='tip'>
        [30]:
        # now we see that argument we used in the barplots are we used the arguments in the boxplot box plot also
        # tells us about the avearge in which mean and emdian is not required
        [28]:
        # import the libraray
        import seaborn as sns

        # set the style
        sns.set_style("whitegrid")

        #load the data set
        tip = sns.load_dataset("tips")
        tip
        # darw the box plot we used the argument of the barplot in the box plot
        sns.boxplot(x="day", y="tip" ,color="red",data=tip,saturation=0.1)
        [28]:
        <Axes: xlabel='day', ylabel='tip'>
        [32]:
        # now we know about the function of the details which is used to describe the data set in details
        [48]:
        # import linraries
        import seaborn as sns
        import pandas as pf
        import numpy as np

        # load the data set
        tip = sns.load_dataset("tips")
        # to decribe the data set which is used in the data set we draw the numeric variable on the y axis and the categorical variable on the x axis
        tip.describe
        [48]:
        <bound method NDFrame.describe of      total_bill   tip     sex smoker   day    time  size
0         16.99  1.01  Female     No   Sun  Dinner     2
1         10.34  1.66    Male     No   Sun  Dinner     3
2         21.01  3.50    Male     No   Sun  Dinner     3
3         23.68  3.31    Male     No   Sun  Dinner     2
4         24.59  3.61  Female     No   Sun  Dinner     4
..          ...   ...     ...    ...   ...     ...   ...
239       29.03  5.92    Male     No   Sat  Dinner     3
240       27.18  2.00  Female    Yes   Sat  Dinner     2
241       22.67  2.00    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
243       18.78  3.00  Female     No  Thur  Dinner     2

[244 rows x 7 columns]>
        [56]:
        # this function is used to decribe the numeric values and their averages and we canot take the numeric values in the hue
        tip.describe()
        [56]:
        total_bill tip size
        count 244.000000 244.000000 244.000000
        mean 19.785943 2.998279 2.569672
        std 8.902412 1.383638 0.951100
        min 3.070000 1.000000 1.000000
        25% 13.347500 2.000000 2.000000
        50% 17.795000 2.900000 2.000000
        75% 24.127500 3.562500 3.000000
        max 50.810000 10.000000 6.000000
        [58]:
        # now we draw the plot for only the single value
        [68]:
        # import the libraray
        import seaborn as sns

        # set the style
        sns.set_style("whitegrid")

        #load the data set
        tip = sns.load_dataset("tips")
        tip
        # darw the box plot we used the argument of the barplot in the box plot
        sns.boxplot(y=tip["total_bill"])
        [68]:
        <Axes: ylabel='total_bill'>
        [70]:
        # now darw the boxplot according to the days
        [78]:
        # import the libraray
        import seaborn as sns

        # set the style
        sns.set_style("whitegrid")

        #load the data set
        tip = sns.load_dataset("tips")
        tip
        # darw the box plot and we add the hue
        sns.boxplot(x="tip" , y="day",hue="smoker", data=tip)
        [78]:
        <Axes: xlabel='tip', ylabel='day'>
        [80]:
        # we add some argument in the draw the box plot
        [90]:
        # import the libraray
        import seaborn as sns

        # set the style
        sns.set_style("whitegrid")

        #load the data set
        tip = sns.load_dataset("tips")
        tip
        # darw the box plot and we add the hue and dodge is the argument in which boolen language is used
        sns.boxplot(x="tip" , y="day",hue="smoker",
        palette="Set2", data=tip , dodge=False)
        [90]:
        <Axes: xlabel='tip', ylabel='day'>
        [94]:
        # now we add the arguments of the color in the draw_boxplot
        [106]:
        # import the libraray
        import seaborn as sns

        # set the style
        sns.set_style("whitegrid")

        #load the data set
        tip = sns.load_dataset("tips")
        tip
        # darw the box plot and we add the color by using hex color picker website
        sns.boxplot(x="tip" , y="day",color="#917f4c",
        data=tip )
        [106]:
        <Axes: xlabel='tip', ylabel='day'>
        [114]:
        # now we draw the function in which shows the just first five line of the data set
        [112]:
        import seaborn as sns
        import pandas as pd
        import numpy as np

        kashti=sns.load_dataset("titanic")
        kashti.head()
        [112]:
        survived pclass sex age sibsp parch fare embarked class who adult_male deck embark_town alive alone
        0 0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
        1 1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
        2 1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
        3 1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
        4 0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True
        [141]:
        import seaborn as sns
        import pandas as pd
        import numpy as np
        import matplotlib.pyplot as plt

        kashti=sns.load_dataset("titanic")
        kashti.head()

        sns.boxplot(x="survived",
        y="age",
        data=kashti)
        [141]:
        <Axes: xlabel='survived', ylabel='age'>
        [122]:
        # now we hightlight the mean by adding the arguments in the draw box plot
        [124]:
        sns.boxplot(x="survived",
        y="age",showmeans=True,
        data=kashti)
        [124]:
        <Axes: xlabel='survived', ylabel='age'>
        [127]:
        # now we making the highlight to the sign of the mean
        [135]:
        sns.boxplot(x="survived",
        y="age",showmeans=True,
        meanprops={"marker":"^",
        "markersize":"12",
        "markeredgecolor":"black"},
        data=kashti)
        [135]:
        <Axes: xlabel='survived', ylabel='age'>
        [137]:
        # now we add the labels in the plot and the design them
        [163]:
        import seaborn as sns
        import pandas as pd
        import numpy as np
        import matplotlib.pyplot as plt
        sns.boxplot(x="survived",
        y="age",showmeans=True,
        meanprops={"marker":"^",
        "markersize":"12",
        "markeredgecolor":"black"},
        data=kashti)
        # show the labels
        plt.xlabel("how many survied"),
        plt.ylabel("Age (years)"),
        plt.title("box_plot")

        [163]:
        Text(0.5, 1.0, 'box_plot')
        [165]:
        # now we add the labels in the plot and the design them and we also change their size
        [169]:
        import seaborn as sns
        import pandas as pd
        import numpy as np
        import matplotlib.pyplot as plt
        sns.boxplot(x="survived",
        y="age",showmeans=True,
        meanprops={"marker":"^",
        "markersize":"12",
        "markeredgecolor":"black"},
        data=kashti)
        # show the labels
        plt.xlabel("how many survied",size=12),
        plt.ylabel("Age (years)",size=12),
        plt.title("box_plot",size=11)

        [169]:
        Text(0.5, 1.0, 'box_plot')
        [171]:
        # now we add the labels in the plot and the design them and we also change their size and also we bold the titels or labels
        [181]:
        import seaborn as sns
        import pandas as pd
        import numpy as np
        import matplotlib.pyplot as plt
        sns.boxplot(x="survived",
        y="age",showmeans=True,
        meanprops={"marker":"^",
        "markersize":"12",
        "markeredgecolor":"black"},
        data=kashti)
        # show the labels
        plt.xlabel("how many survied",size=12,weight="bold"),
        plt.ylabel("Age (years)",size=12,weight="bold"),
        plt.title("box_plot",size=11,weight="bold")

        [181]:
        Text(0.5, 1.0, 'box_plot')
        • test_jupyter_notebook
        • indexing and data structure
        • 02_bar_plot
        • 03_boxplot
        • 04_otherplots
        • 05_interactive
        • 06_array
        Kernel status: Idle Executed 3 cellsElapsed time: 7 seconds
        # lineplot_withmultifacet
        - and we found these plots from the seaborn website

        lineplot_withmultifacet¶

        • and we found these plots from the seaborn website
        [3]:
        # time series plot
        [9]:
        import seaborn as sns
        sns.set_theme(style="darkgrid")

        # Load an example dataset with long-form data
        fmri = sns.load_dataset("fmri")
        fmri.head()
        [9]:
        subject timepoint event region signal
        0 s13 18 stim parietal -0.017552
        1 s5 14 stim parietal -0.080883
        2 s12 18 stim parietal -0.081033
        3 s11 18 stim parietal -0.046134
        4 s10 18 stim parietal -0.037970
        [5]:
        import seaborn as sns
        sns.set_theme(style="darkgrid")

        # Load an example dataset with long-form data
        fmri = sns.load_dataset("fmri")

        # Plot the responses for different events and regions
        sns.lineplot(x="timepoint", y="signal",
        hue="region", style="event",
        data=fmri)
        [5]:
        <Axes: xlabel='timepoint', ylabel='signal'>
        [11]:
        # line plot on multiple facts
        [13]:
        import seaborn as sns
        sns.set_theme(style="ticks")

        dots = sns.load_dataset("dots")
        dots.head()
        [13]:
        align choice time coherence firing_rate
        0 dots T1 -80 0.0 33.189967
        1 dots T1 -80 3.2 31.691726
        2 dots T1 -80 6.4 34.279840
        3 dots T1 -80 12.8 32.631874
        4 dots T1 -80 25.6 35.060487
        [15]:
        # Define the palette as a list to specify exact values
        palette = sns.color_palette("rocket_r")

        # Plot the lines on two facets
        sns.relplot(
        data=dots,
        x="time", y="firing_rate",
        hue="coherence", size="choice", col="align",
        kind="line", size_order=["T1", "T2"], palette=palette,
        height=5, aspect=.75, facet_kws=dict(sharex=False),
        )
        [15]:
        <seaborn.axisgrid.FacetGrid at 0x1f42cc07770>
        Kernel status: Idle Executed 4 cellsElapsed time: 6 seconds
        [1]:
        pip install plotly
        Requirement already satisfied: plotly in c:\users\rana azam\anaconda3\asad\lib\site-packages (5.22.0)
Requirement already satisfied: tenacity>=6.2.0 in c:\users\rana azam\anaconda3\asad\lib\site-packages (from plotly) (8.2.2)
Requirement already satisfied: packaging in c:\users\rana azam\anaconda3\asad\lib\site-packages (from plotly) (23.2)
Note: you may need to restart the kernel to use updated packages.
        [17]:
        # by installing the pip plotly we can form intractive plot one of the example is given below
        [9]:
        import plotly.express as px

        phool = px.data.iris()
        phool.head()
        [9]:
        sepal_length sepal_width petal_length petal_width species species_id
        0 5.1 3.5 1.4 0.2 setosa 1
        1 4.9 3.0 1.4 0.2 setosa 1
        2 4.7 3.2 1.3 0.2 setosa 1
        3 4.6 3.1 1.5 0.2 setosa 1
        4 5.0 3.6 1.4 0.2 setosa 1
        [25]:
        import plotly.express as px

        phool = px.data.iris()
        phool.head()
        fig = px.scatter(phool, x="sepal_width" ,y="sepal_length", color="species",marginal_y="violin",
        marginal_x="box",trendline="ols",template="simple_white")
        fig.show()
        22.533.544.545678
        speciessetosaversicolorvirginicasepal_widthsepal_length
        plotly-logomark
        Kernel status: Idle
        # 1D array
        - we can aslo called the vector to the 1D array

        1D array¶

        • we can aslo called the vector to the 1D array
        [6]:
        # import the libraray
        import numpy as np
        a= np.array([5,5,5])
        a
        [6]:
        array([5, 5, 5])
        [8]:
        # to find the type of the array
        [10]:
        type(a)
        [10]:
        numpy.ndarray
        [12]:
        # to find the length of the array
        [14]:
        len(a)
        [14]:
        3
        [16]:
        # now wwe done the indexing the array or location of the element
        [18]:
        a[0]
        [18]:
        5
        [20]:
        # we can also take the output by doing the given below action
        [24]:
        a[0:4]
        [24]:
        array([5, 5, 5])
        [46]:
        # now we create there some types of the array
        [62]:
        # these are the examples of 1D array
        [48]:
        import numpy as np
        a = np.array([1,2,3,4,5])
        a
        [48]:
        array([1, 2, 3, 4, 5])
        [52]:
        # when we want the zeros in the array then we done this type of the code
        [56]:
        b = np.zeros(2)
        b
        [56]:
        array([0., 0.])
        [58]:
        # when we want the ones in the array then we done this type of the code
        [60]:
        c = np.ones(5)
        c
        [60]:
        array([1., 1., 1., 1., 1.])
        [66]:
        # we can create also the empty array with 3 elemnts or more
        [74]:
        d = np.empty(1)
        d
        [74]:
        array([2.4e-322])
        [80]:
        # we can also create the array by given the range
        [78]:
        e = np.arange(6)
        e
        [78]:
        array([0, 1, 2, 3, 4, 5])
        [82]:
        # we can also create the array by given the specific range(from this to this)
        [86]:
        f = np.arange(2,15)
        f
        [86]:
        array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])
        [88]:
        # we can also create the array by give the specific number and thier difference
        [92]:
        g = np.arange(2,20,2)
        g
        [92]:
        array([ 2,  4,  6,  8, 10, 12, 14, 16, 18])
        [94]:
        # we can also create linerly spaced arraya
        [100]:
        h = np.linspace(0,10,num=5) # give use 5 nums
        h
        [100]:
        array([ 0. ,  2.5,  5. ,  7.5, 10. ])
        [102]:
        # we use the array to obtain the output in specific data by using the this formula
        [104]:
        i = np.ones(5, dtype=np.int8)
        i
        [104]:
        array([1, 1, 1, 1, 1], dtype=int8)
        [108]:
        i = np.ones(3, dtype=np.float64)
        i
        [108]:
        array([1., 1., 1.])
        # 2D array
        - we can also called the matrices to the 2D array

        2D array¶

        • we can also called the matrices to the 2D array
        [115]:
        import numpy as np
        b = np.array([[5, 5, 5],[5, 5, 5],[5, 5, 5]])
        b
        [115]:
        array([[5, 5, 5],
       [5, 5, 5],
       [5, 5, 5]])
        [117]:
        type(b)

        [117]:
        numpy.ndarray
        [119]:
        len(b)

        [119]:
        3
        [123]:
        # indexing of the array
        [121]:
        b[0]
        [121]:
        array([5, 5, 5])
        [125]:
        b[0:]
        [125]:
        array([[5, 5, 5],
       [5, 5, 5],
       [5, 5, 5]])
        [127]:
        b[0:3]
        [127]:
        array([[5, 5, 5],
       [5, 5, 5],
       [5, 5, 5]])
        # in 2D array when we talk about the axis first we include the column and then row e.g(2,4) in this 2 is the column and 4 is the row

        in 2D array when we talk about the axis first we include the column and then row e.g(2,4) in this 2 is the column and 4 is the row¶

        [130]:
        np.zeros((3,4)) # 3 means rows and 4 mean columns
        [130]:
        array([[0., 0., 0., 0.],
       [0., 0., 0., 0.],
       [0., 0., 0., 0.]])
        [134]:
        np.ones((6,5))
        [134]:
        array([[1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.]])
        [136]:
        np.empty((3,4))
        [136]:
        array([[0., 0., 0., 0.],
       [0., 0., 0., 0.],
       [0., 0., 0., 0.]])
        # 3D array

        3D array¶

        [149]:
        i = np.arange(24).reshape(2,3,4) # in which 2 is number of the matrices and 3 is coimuns and 4 is the rows
        i
        [149]:
        array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],

       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]]])
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          Python 3 (ipykernel) | Idle
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          06_array
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          Ln 1, Col 9
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